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247 520 (0,285s)

Result

Parameter Reusing in Learning Latent Class Models

Basic themes of document: latent class model; hidden nodes; learning...

JD - Využití počítačů, robotika a její aplikace

  • 2004
  • D
Result

Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions

Learning parameters of a probabilistic model is a necessary step in most machine learning modeling tasks. When the model is complex and data volume is small which are restrictions on parameter...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2018
  • D
Result

Using Machine Learning to Predict Optimal Parameters in Portfolio Optimization Problems

create machine learning model to predict the optimal values of such parameter optimization model and xgboost machine learning model. Extensive simulations were performed is presented, showing tha...

Applied mathematics

  • 2020
  • D
Result

Municipal Creditworthiness Modelling by Kohonen´s Self-organizing Feature Maps and LVQ Neural Networks

The paper presents the design of municipal creditworthiness parameters. Further, a model is designed based on Learning Vector Quantization neural networks for municipal creditworthiness classification. The model is...

AE - Řízení, správa a administrativa

  • 2008
  • D
Result

Learning bipartite Bayesian networks under monotonicity restrictions

Learning parameters of a probabilistic model is a necessary step in machine by users. We present an algorithm for Bayesian Networks parameter learning. Learned models are compared with re...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2020
  • Jimp
  • Link
Result

Inverse kinematic model of robotic leg created by using Lazy Learning method

This paper deals with the application of local modelling method Lazy Learning by using The Lazy Learning Toolbox for Use with Matlab. The method is applied to solve the inverse kinematic problem, direct kinematic model<...

BC - Teorie a systémy řízení

  • 2002
  • D
Result

Unsupervised (parameter) learning for MRFs on bipartite graphs

We consider unsupervised (parameter) learning for general Markov random fields on bipartite graphs. This model class includes Restricted Boltzmann Machines. We show- trastive Divergence) there is an alternative learning...

JD - Využití počítačů, robotika a její aplikace

  • 2013
  • D
  • Link
Result

Combining Parameter Space Search and Meta-learning for Data-Dependent Computational Agent Recommendation

experiments without the necessary knowledge of the most suitable machine learning method and its parameters to the data. In order to replace the expert's knowledge, the meta-learning subsystems are proposed including the <...

IN - Informatika

  • 2012
  • D
  • Link
Result

Air Quality Modelling by Neural Networks

The chapter presents the parameters design for air quality modelling. Only those parameters were selected which show low correlation dependences. Therefore (KSOFM) (unsupervised learning) and Learning Vect...

IN - Informatika

  • 2009
  • C
Result

Monotonicity in Bayesian Networks for Computerized Adaptive Testing

. The question of effectively learning parameters of such models even with small data samples an algorithm for learning model parameters, which satisfy monotonicity conditions, based by Masegosa e...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2017
  • D
  • Link
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